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Analysis of ingredient relationships of food genome’s 1 million recipes. Uses ingredient intersection across courses for cooks to save time and money.

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Background

My parents live in a rural area, and sometimes need to drive for 1 hour to get 1 or 2 ingredients for dinner.

Challenge

Remove some of the manual and cognitive labor around making dinner. Make a meal planner that helps people like my parents save money by making dishes sharing ingredients, and save time by avoiding single item trips.

Approach

First I found an API (yummly.com) with 1 million recipes and features like grocery list, recipe name, ID, cuisine, and course (ex: dinner, lunch, salads). I did exploratory analysis and cleaning using Python, Pandas and MongoDB. I graphed ingredient networks and recipe networks with NetworkX.

Outcome

The result is a meal planner that uses distance, and co-occurrence of ingredients to generate dinner plans.

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Analysis of ingredient relationships of food genome’s 1 million recipes. Uses ingredient intersection across courses for cooks to save time and money.

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